GGplot2 Examples
EM
2023-02-22
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summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00We can load in our own data just as before, titling the code
chunk (here I called it “data”) and if we don’t want the code chunk
to be printed out, we can add echo = FALSE to the
header.
## Cross_ID Cross_Type Sample_ID Dam_Weight_g
## Length:523 Length:523 Length:523 Min. :13.68
## Class :character Class :character Class :character 1st Qu.:20.50
## Mode :character Mode :character Mode :character Median :22.83
## Mean :23.18
## 3rd Qu.:26.16
## Max. :30.21
##
## Dam_Body_mm Dam_Just_body Dam_Tail_mm Dam_Foot_mm
## Min. : 82.65 Min. :70.38 Min. : 0.00 Min. :11.60
## 1st Qu.:137.37 1st Qu.:78.90 1st Qu.:58.32 1st Qu.:15.27
## Median :145.97 Median :82.28 Median :63.79 Median :16.11
## Mean :142.31 Mean :81.65 Mean :60.96 Mean :16.09
## 3rd Qu.:154.08 3rd Qu.:84.53 3rd Qu.:68.65 3rd Qu.:16.98
## Max. :166.53 Max. :94.16 Max. :78.22 Max. :18.97
## NA's :5
## Dam_Ear_mm Embryo_weight_g Placenta_Weight_g Side_of_Repro
## Min. : 7.81 Min. :0.0723 Min. :0.02120 Length:523
## 1st Qu.: 9.62 1st Qu.:0.6038 1st Qu.:0.08260 Class :character
## Median :10.37 Median :0.7368 Median :0.09490 Mode :character
## Mean :10.39 Mean :0.7808 Mean :0.09994
## 3rd Qu.:11.04 3rd Qu.:0.9670 3rd Qu.:0.10750
## Max. :14.00 Max. :2.1764 Max. :0.36850
##
## TS day sex N_in_litter
## Min. :22.00 Min. :14.00 Length:523 Min. : 1.00
## 1st Qu.:24.00 1st Qu.:16.00 Class :character 1st Qu.: 4.00
## Median :24.50 Median :16.50 Mode :character Median : 6.00
## Mean :24.57 Mean :16.56 Mean : 5.47
## 3rd Qu.:25.00 3rd Qu.:17.00 3rd Qu.: 7.00
## Max. :26.50 Max. :18.50 Max. :10.00
##
## Experiment
## Length:523
## Class :character
## Mode :character
##
##
##
## GGplot2, or using similar syntax for a bunch of different plot types!
First, we are going to build the simplest of scatter plots
ggplot(data = example_data, aes(x = Dam_Weight_g, y = Dam_Just_body)) + geom_point(alpha = .2)
Now let’s make our code easier to read!
example_data %>%
ggplot(aes(x = Dam_Weight_g, y = Dam_Just_body)) +
geom_point()
We can get also add more features to the plot.
example_data %>%
ggplot(aes(x = Dam_Weight_g, y = Dam_Just_body)) +
geom_point()+
geom_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
example_data %>%
ggplot(aes(x = Dam_Weight_g, y = Dam_Just_body, color = Experiment)) +
geom_point()+
geom_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
We can get fancier than this. Let’s add some more categories to
aesthetics
example_data %>%
ggplot(aes(x = Dam_Weight_g, y = Dam_Just_body, color = Cross_Type, shape = Experiment)) +
geom_point()
Let’s try another plot type, with categorical data
library(beeswarm)
example_data %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_point()
example_data %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_boxplot(width = 0.2, color = "black")+
geom_quasirandom()
Let’s keep on making our plot fancy! You can change the background and gridlines quickly by using themes, update the angle of labels, switch the axis titles, etc.
example_data %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom()+
geom_boxplot(width=0.2, color = "black", alpha = 0.01)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")
This is nice, but our variables aren’t ordered in our graph in a meaningful way. Let’s fix that!
library(forcats)
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom()+
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")
Ugh, these colors tho. Lets try some other color options! R color brewer
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom()+
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_brewer(palette = "Blues")
We can also set colors manually if you have hex codes or color names:
spret_color <-"#CACACA"
mus_color <- "#949494"
dom_color <-"#535360"
mus_dom_color <-"#74b3ce"
mus_spret_color <-"#94C973"
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Placenta_Weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom()+
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_manual(values = c(dom_color,
mus_color,
mus_dom_color,
mus_spret_color,
spret_color))
We can also use other packages with fun palettes, like Wes Anderson films or national parks
library(wesanderson)
library(NatParksPalettes)## Registered S3 method overwritten by 'NatParksPalettes':
## method from
## print.palette wesanderson######Wes Anderson####
pal <- wes_palette("FantasticFox1", n = 5)
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_manual(values = pal)
######National Parks####
pal <- natparks.pals("Yellowstone", n = 5)
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_manual(values = pal)
We can also color by continuous variables, either already plotted or in our data frame
######National Parks####
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Placenta_Weight_g)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_gradientn(colors=natparks.pals("Glacier"))
What if we want to highlight data groups in different panels? One way is to use facets
pal <- natparks.pals("Yellowstone", n = 5)
example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_manual(values = pal)+
facet_grid(Experiment ~ ., scales = "free")
We can also save out plots as objects, and then further make our panels
nice!
pal <- natparks.pals("Yellowstone", n = 5)
Embryo_plot <- example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Embryo_weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5), legend.position = "none")+
ylab("embryo weight (g)")+
xlab("cross")+
scale_color_manual(values = pal)
Placenta_plot <- example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Cross_Type, y = Placenta_Weight_g, color = Experiment)) +
geom_violin()+
geom_quasirandom() +
geom_boxplot(width=0.2, color = "black")+
theme_cowplot()+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+
ylab("placenta weight (g)")+
xlab("cross")+
ylim(0,.4)+
scale_color_manual(values = pal)
plot_grid(Embryo_plot, Placenta_plot, labels = c("A","B"),rel_widths = c(1, 2))
GGplot2 can make so many more things!!!!
library(ggridges)
ridges <- example_data %>%
mutate(Cross_Type = fct_relevel(Cross_Type,
"TF.FM", "FM.TF",
"CC.PP", "PP.CC",
"WW.LL", "LL.WW",
"PP.WW", "WW.PP",
"CC.WW", "WW.CC",
"PP.LL", "LL.PP",
"CC.LL", "LL.CC",
"PP.TF", "TF.PP")) %>%
ggplot(aes(x = Placenta_Weight_g, y = Cross_Type, fill = Experiment)) +
geom_density_ridges(jittered_points = TRUE,
position = position_points_jitter(width = 0.05, height = 0),
point_shape = '|',
point_size = 3,
point_alpha = 1,
alpha = 0.5) +
theme_ridges() +
theme(legend.position = "none")+
#ggtitle("")+
theme_bw()+
xlab("placenta weight (g)")+
ylab("cross")
ridges## Picking joint bandwidth of 0.00595
We can also make plots interactive, to see the identity of specific points
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(Placenta_plot)